Name
Abstract | ||
|---|---|---|
The Support Vector Machine (SVM) is one of the most effective and used algorithms, when targeting classification. Despite its large success, SVM is mainly afflicted by two issues: (i) some hyperparameters must be tuned in advance and are, in practice, identified through computationally intensive procedures; (ii) possible a-priori knowledge about the problem (e.g. doctor expertise in medical applications) cannot be straightforwardly exploited. In this paper, we introduce a new approach, able to cope with the two previous problems: several experiments, performed on real-world benchmarking datasets, show that our method outperforms, on average, other techniques proposed in the literature. |
Year | Venue | Field |
|---|---|---|
2015 | 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | Structured support vector machine,Data mining,Hyperparameter,Ranking SVM,Computer science,Support vector machine,Software,Artificial intelligence,Machine learning,Benchmarking |
DocType | ISSN | Citations |
Conference | 2161-4393 | 0 |
PageRank | References | Authors |
0.34 | 25 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Oneto | 1 | 830 | 63.22 |
| Alessandro Ghio | 2 | 667 | 35.71 |
| Sandro Ridella | 3 | 677 | 140.62 |
| Davide Anguita | 4 | 1001 | 70.58 |